AI Evaluation of Imaging Factors in the Evolution of Stage-Treated Metastases Using Gamma Knife.

BACKGROUND: The study investigated whether three deep-learning models, namely, the CNN_model (trained from scratch), the TL_model (transfer learning), and the FT_model (fine-tuning), could predict the early response of brain metastases (BM) to radiosurgery using a minimal pre-processing of the MRI images. The dataset consisted of 19 BM patients who underwent stereotactic-radiosurgery (SRS) within 3 months. The images used included axial fluid-attenuated inversion recovery (FLAIR) sequences and high-resolution contrast-enhanced T1-weighted (CE T1w) sequences from the tumor center. The patients were classified as responders (complete or partial response) or non-responders (stable or progressive disease). METHODS: A total of 2320 images from the regression class and 874 from the progression class were randomly assigned to training, testing, and validation groups. The DL models were trained using the training-group images and labels, and the validation dataset was used to select the best model for classifying the evaluation images as showing regression or progression. RESULTS: Among the 19 patients, 15 were classified as "responders" and 4 as "non-responders". The CNN_model achieved good performance for both classes, showing high precision, recall, and F1-scores. The overall accuracy was 0.98, with an AUC of 0.989. The TL_model performed well in identifying the "progression" class, but could benefit from improved precision, while the "regression" class exhibited high precision, but lower recall. The overall accuracy of the TL_model was 0.92, and the AUC was 0.936. The FT_model showed high recall for "progression", but low precision, and for the "regression" class, it exhibited a high precision, but lower recall. The overall accuracy for the FT_model was 0.83, with an AUC of 0.885. CONCLUSIONS: Among the three models analyzed, the CNN_model, trained from scratch, provided the most accurate predictions of SRS responses for unlearned BM images. This suggests that CNN models could potentially predict SRS prognoses from small datasets. However, further analysis is needed, especially in cases where class imbalances exist.

FIP1L1-PDGFRα-Positive Loeffler Endocarditis-A Distinct Cause of Heart Failure in a Young Male: The Role of Multimodal Diagnostic Tools.

The presence of the Fip1-Like1-platelet-derived growth factor receptor alpha (FIP1L1-PDGFRα) fusion gene represents a rare cause of hypereosinophilic syndrome (HES), which is associated with organ damage. The aim of this paper is to emphasize the pivotal role of multimodal diagnostic tools in the accurate diagnosis and management of heart failure (HF) associated with HES. We present the case of a young male patient who was admitted with clinical features of congestive HF and laboratory findings of hypereosinophilia (HE). After hematological evaluation, genetic tests, and ruling out reactive causes of HE, a diagnosis of positive FIP1L1-PDGFRα myeloid leukemia was established. Multimodal cardiac imaging identified biventricular thrombi and cardiac impairment, thereby raising suspicion of Loeffler endocarditis (LE) as the cause of HF; this was later confirmed by a pathological examination. Despite hematological improvement under corticosteroid and imatinib therapy, anticoagulant, and patient-oriented HF treatment, there was further clinical progression and subsequent multiple complications (including embolization), which led to patient death. HF is a severe complication that diminishes the demonstrated effectiveness of imatinib in the advanced phases of Loeffler endocarditis. Therefore, the need for an accurate identification of heart failure etiology in the absence of endomyocardial biopsy is particularly important for ensuring effective treatment.

Immunotherapy and radiotherapy - a future partnership Focus on radiation induced lymphopenia (RIL) implications.

The hematological toxicity associated with radiotherapy is represented by neutropenia, anemia, thrombocytopenia, being associated with the increased risk of infection with opportunists, with fatigue and intolerance to effort, but also with the increased risk of bleeding. In the context of the preclinical and clinical results that mention the synergistic effect of the immunotherapy-radiotherapy association, radiation-induced lymphopenia (RIL) becomes an immunosuppression factor, a factor that would tip the fragile antitumor immunopotentiation-immunosuppression balance in favor of the immunosuppressive effect. Both the number of lymphocytes and the neutrophil/lymphocyte ratio (NLR) are prognostic and predictive biomarkers, providing information on the immune status of the host and on a possible response of the tumor to immunotherapy. Modern radiation techniques can increase the risk of lymphopenia by irradiating large volumes of tissue with low doses of radiation. In this context, a redefinition of the dose-volume constraints and the definition of bone marrow, lymphoid organs and lymph nodes not involved in tumors as organs at risk (OARs) is strictly necessary in the case of using irradiation through intensity-modulated radiation therapy (IMRT) techniques or volumetric modulated arc therapy (VMAT) for solid tumors that benefit from immune checkpoint inhibitor (ICI) therapy (Ref. 22). Text in PDF www.elis.sk Keywords: lymphopenia, immunotherapy, radiotherapy, toxicity, spleen, nodes irradiation.

Hypofractionated Whole-Breast Irradiation Focus on Coronary Arteries and Cardiac Toxicity-A Narrative Review.

Breast cancer is the most common cancer among women worldwide, which is often treated with radiotherapy. Whole breast irradiation (WBI) is one of the most common types of irradiation. Hypo-fractionated WBI (HF-WBI) reduces the treatment time from 5 to 3 weeks. Recent radiobiological and clinical evidence recommended the use of HF-WBI regardless of the age or stage of disease, and it is proven that hypo-fractionation is non-inferior to conventional fractionation regimen irradiation. However, some studies report an increased incidence of heart-related deaths in the case of breast irradiation by hypo-fractionation, especially in patients with pre-existing cardiac risk factors at the time of treatment. Due to the new technical possibilities of radiotherapy techniques, HF-WBI can reduce the risk of cardiac toxicity by controlling the doses received both by the heart and by the anatomical structures of the heart. The radiobiological "double trouble", in particular "treble trouble", for hypo-fractionated regimen scan be avoided by improving the methods of heart sparing based on image-guided irradiation (IGRT) and by using respiration control techniques so that late cardiac toxicity is expected to be limited. However, long-term follow-up of patients treated with HF-WBI with modern radiotherapy techniques is necessary considering the progress of systemic therapy, which is associated with long-term survival, and also the cardiac toxicity of new oncological treatments. The still unknown effects of small doses spread in large volumes on lung tissue may increase the risk of second malignancy, but they can also be indirectly involved in the later development of a heart disease. It is also necessary to develop multivariable radiobiological models that include histological, molecular, clinical, and therapeutic parameters to identify risk groups and dosimetric tolerance in order to limit the incidence of late cardiac events. MR-LINAC will be able to offer a new standard for reducing cardiac toxicity in the future, especially in neoadjuvant settings for small tumors.

Radiobiological approach to treatment gaps in locally advanced head and neck cancers radical radiotherapy arising from the COVID-19 pandemic.

Gaps of radiotherapy treatment are one of the factors recognized as unfavorable in terms of tumor control and disease prognosis. All strategies for compensating the negative effect of radiotherapy treatment gaps are based on radiobiological models. Using the modified square linear formalism (Dale`s equation) it is possible to calculate the additional dose in order to compensate the accelerated tumor repopulation effect. SARS-CoV-2 infection is an important factor that can lead to an interruption of irradiation for medium and long- term intervals. We aim to present the radiobiological data underlying the recalculation of radiotherapy treatment and exemplification for different clinical scenarios in the case of head and neck cancers (Ref. 17). Keywords: adiobiology, treatment gaps, locally advanced head and neck cancers, radical radiotherapy, COVID-19 pandemic.

Native Aortic Valve Endocarditis Complicated by Splenic Infarction and Giant Mitral-Aortic Intervalvular Fibrosa Pseudoaneurysm-A Case Report and Brief Review of the Literature.

BACKGROUND: Pseudoaneurysm of the mitral-aortic intervalvular fibrosa (P-MAIVF) is an unusual complication related to various injuries or conditions which involve the mitro-aortic region; it communicates with the left ventricular outflow tract and is associated with a high-risk of redoubtable complications or sudden death. The cerebral and splenic localizations are frequently seen as manifestations of systemic embolism in infective endocarditis. Currently, there are no specific recommendations related to the diagnosis, management, treatment, or further evolution of patients with P-MAIVF and concomitant splenic infarction. This paper presents the case of a 43-year-old Caucasian woman with a late diagnosis of mixed bicuspid aortic valve disease, affected by an under-detected and undertreated episode of infective endocarditis leading to asymptomatic P-MAIVF. Prime clinical and imagistic diagnosis of splenic infarction indicated further extended investigations were required to clarify the source of embolism. METHODS: Integrated multimodality imaging techniques confirmed the unexpected diagnosis of P-MAIVF. RESULTS: The case had a fatal outcome following an uncomplicated yet laborious cardiac surgery. Patient death was attributed to a malignant ventricular arrhythmia. CONCLUSION: The present case raises awareness by highlighting an unexplained and unexpected splenic infarction association with P-MAIVF as a result of infective endocarditis related to mixed bicuspid aortic valve disease.

Radiomic Machine Learning and Texture Analysis - New Horizons for Head and Neck Oncology.

Radiomics is a relatively new concept that consists of extracting data from images and applies advanced characterization algorithms to generate imaging features. These features are biomarkers with prognostic and predictive value, which provide a characterization of tumor phenotypes in a non-invasive manner. The clinical application of radiomics is hampered by challenges such as lack of image acquisition and analysis standardization. Textural features extracted from computed tomography (CT), magnetic resonance imaging (MRI) and positron emission tomography-computed tomography (PET-CT) images of patients diagnosed with head and neck cancers can be used in the pre-therapeutic evaluation of the response to multimodal chemo-radiotherapy. For patients with positive HPV-oropharyngeal cancers, the correlation of the radiomic textural features from the tumor with p16 values from the pathological sample can identify tumor specific signatures in CT imaging, an entity with favorable prognosis and a better response to chemo-radiotherapy. Pretreatment contrast CT-scans were extracted and radiomics analysis of gross tumor volume were performed using MaZda package apart from MaZda software containing B11 program for texture analysis and visualization. Data set was randomly divided into a training dataset and a test dataset and machine learning algorithms were applied to identify a textural radiomic signature. Radiomic texture analysis and machine learning algorithms demonstrate a predictive potential related to the capability of stratification for subclasses of platinum-chemotherapy resistance and radioresistant head and neck cancers requiring an intensification of multimodal treatment.